Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data

Lauze, François; Quéau, Yvain; Plenge, Esben

doi:10.1007/978-3-319-58771-4_25

Cited by 11 publications

(10 citation statements)

References 12 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image segmentation and 3D-reconstruction may appear as two disconnected problems. Nevertheless, each task contributes information which may be interesting for the other, and the joint solving of these inverse problems has proven valuable, for instance, in the context of dense multiview reconstruction [7], X-ray tomography [9], pose estimation [18], SLAM [25] or hyperspectral imaging [28]. Inspired by such joint approaches to simultaneous reconstruction and segmentation, in the rest of this work we revisit the photometric stereo problem in the case where no prior segmentation of the object has been performed i.e., domain Ω in (1) is unknown.…”

Section: Variational Methods For Photometric Stereo and Segmentationmentioning

confidence: 99%

Photometric Segmentation: Simultaneous Photometric Stereo and Masking

Haefner

Quéau

Cremers

2019

2019 International Conference on 3D Vision (3DV)

View full text Add to dashboard Cite

This work is concerned with both the 3D-reconstruction of an object using photometric stereo, and its 2Dsegmentation from the background. In contrast with previous works on photometric stereo which assume that a mask of the area of interest has been computed beforehand, we formulate 3D-reconstruction and 2D-segmentation as a joint problem. The proposed variational solution combines a differential formulation of photometric stereo with the classic Chan-Vese model for active contours. Given a set of photometric stereo images, this solution simultaneously infers a binary mask of the object of interest and a depth map representing its 3D-shape. Experiments on real-world datasets confirm the soundness of simultaneously solving both these classic computer vision problems, as the joint approach considerably simplifies the overall 3D-scanning process for the end-user.

show abstract

Section: Variational Methods For Photometric Stereo and Segmentationmentioning

confidence: 99%

Photometric Segmentation: Simultaneous Photometric Stereo and Masking

Haefner

Quéau

Cremers

2019

2019 International Conference on 3D Vision (3DV)

View full text Add to dashboard Cite

show abstract

“…This splitting turns the problem into two simple problems solved by the conjugate gradient method and a submodular minimization solver. In [9] the authors suggested a joint reconstruction and segmentation method in a variational framework. Other techniques include graph cut [10], [11] and convex relaxation techniques [12].…”

Section: A Related Workmentioning

confidence: 99%

DALM, Deformable Attenuation-Labeled Mesh for Tomographic Reconstruction and Segmentation

Koo

Dahl

2021

IEEE Trans. Comput. Imaging

View full text Add to dashboard Cite

Most X-ray tomographic reconstruction methods represent a solution as an image on a regular grid. Such representation may be inefficient for reconstructing homogeneous objects from noisy or incomplete projections. Here, we propose a mesh-based method for reconstruction and segmentation of homogeneous objects directly from sinogram data. The outcome of our proposed method consists of curves outlining the regions of constant attenuation, and this output is represented using a labeled irregular triangle mesh. We find the solution by deforming the mesh to minimize the residual given by the sinogram data. Our method supports multiple materials, and allows for topological changes during deformation. An integral part of our algorithm is an efficient forward projection of the labeled mesh onto the sinogram domain. We initialize our algorithm based on graph total variation, also here taking advantage of the mesh representation. Experimental results on simulated datasets show that our method gives a compact representation of the reconstruction and also accurate segmentation results for challenging data with e.g. large noise, a small number of angles or problems with limited angle. We also demonstrate the result on real fan-beam data. The proposed geometric solution shows a further step towards using alternative representations for tomographic reconstruction.

show abstract

“…SPECT), or the segmentation term. In [31,32,34] the authors model the segmentation as a mixture of Gaussian distribution, while [30] has a a region-based segmentation approach similar to what we propose. However, [30] penalises the squared 2-norm of segmentation, imposing spatial smoothness.…”

Section: Comparison To Other Joint Reconstruction and Segmentation Apmentioning

confidence: 99%

“…In [31,32,34] the authors model the segmentation as a mixture of Gaussian distribution, while [30] has a a region-based segmentation approach similar to what we propose. However, [30] penalises the squared 2-norm of segmentation, imposing spatial smoothness. In our proposed joint approach, we perform reconstruction and segmentation in a unified Bregman iteration scheme, exploiting the advantage of improving the reconstruction, which results in a more accurate segmentation.…”

Section: Comparison To Other Joint Reconstruction and Segmentation Apmentioning

confidence: 99%

Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

et al. 2019

View full text Add to dashboard Cite

All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. Recently, the idea of tackling both problems jointly has been proposed. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variational regularisation scheme to a Bregman iteration framework to improve the reconstruction and therefore the segmentation. We develop a novel alternating minimisation scheme that solves the non-convex optimisation problem with provable convergence guarantees. Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the classical sequential approach.

show abstract

Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data

Cited by 11 publications

References 12 publications

Photometric Segmentation: Simultaneous Photometric Stereo and Masking

Photometric Segmentation: Simultaneous Photometric Stereo and Masking

DALM, Deformable Attenuation-Labeled Mesh for Tomographic Reconstruction and Segmentation

Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

Contact Info

Product

Resources

About